scholarly journals Analysis of Behavioral Economics in Crowdsensing: A Loss Aversion Cooperation Model

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Deng Li ◽  
Liying Qiu ◽  
Jiaqi Liu ◽  
Congwen Xiao
Keyword(s):  
Behavioral Economics ◽  
Utility Function ◽  
Expected Utility ◽  
Loss Aversion ◽  
Incentive Mechanism ◽  
Experimental Results ◽  
Individual Utility ◽  
Incentive Mechanisms ◽  
Cooperation Model

The existing incentive mechanisms of crowdsourcing construct the expected utility function based on the assumption of rational people in traditional economics. A large number of studies in behavioral economics have demonstrated the defects of the traditional utility function and introduced a new parameter called loss aversion coefficient to calculate individual utility when it suffers a loss. In this paper, combination of behavioral economics and a payment algorithm based on the loss aversion is proposed. Compared with usual incentive mechanisms, the node utility function is redefined by the loss aversion characteristic of the node. Experimental results show that the proposed algorithm can get a higher rate of cooperation with a lower payment price and has good scalability compared with the traditional incentive mechanism.

scholarly journals Cooperation Promotion from the Perspective of Behavioral Economics: An Incentive Mechanism Based on Loss Aversion in Vehicular Ad-Hoc Networks

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 225 ◽  
Author(s):  
Jiaqi Liu ◽  
Shiyue Huang ◽  
Hucheng Xu ◽  
Deng Li ◽  
Nan Zhong ◽  
...  
Keyword(s):  
Ad Hoc Networks ◽  
Loss Aversion ◽  
High Speed ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Incentive Mechanism ◽  
Final Decision ◽  
The Incentive Mechanism ◽  
Hoc Networks

As a special mobile ad-hoc network, Vehicular Ad-hoc Networks (VANETs) have the characteristics of high-speed movement, frequent topology changes, multi-hop routing, a lack of energy, storage space limitations, and the possible selfishness of the nodes. These characteristics bring challenges to the design of the incentive mechanism in VANETs. In the current research on the incentive mechanism of VANETs, the mainstream is the reward-based incentive mechanism. Most of these mechanisms are designed based on the expected utility theory of traditional economics and assume that the positive and negative effects produced by an equal amount of gain and loss are equal in absolute value. However, the theory of loss aversion points out that the above effects are not equal. Moreover, this will lead to a deviation between the final decision-making behavior of nodes and the actual optimal situation. Therefore, this paper proposed a Loss-Aversion-based Incentive Mechanism (LAIM) to promote the comprehensive perception and sharing of information in the VANETs. This paper designs the incentive threshold and the threshold factor to motivate vehicle nodes to cooperate. Furthermore, based on the number of messages that the nodes face, the utility function of nodes is redesigned to correct the assumption that a gain and a loss of an equal amount could offset each other in traditional economics. The simulation results show that compared with the traditional incentive mechanism, the LAIM can increase the average utility of nodes by more than 34.35%, which promotes the cooperation of nodes.

scholarly journals Coping with Loss Aversion in the Newsvendor Model

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Jianwu Sun ◽  
Xinsheng Xu
Keyword(s):  
Expected Utility ◽  
Loss Aversion ◽  
Confidence Level ◽  
Profit Maximization ◽  
Optimal Order ◽  
Market Demand ◽  
Newsvendor Model ◽  
Order Quantity ◽  
Decision Bias ◽  
Optimal Order Quantity

We introduce loss aversion into the decision framework of the newsvendor model. By introducing the loss aversion coefficientλ, we propose a novel utility function for the loss-averse newsvendor. First, we obtain the optimal order quantity to maximize the expected utility for the loss-averse newsvendor who is risk-neutral. It is found that this optimal order quantity is smaller than the expected profit maximization order quantity in the classical newsvendor model, which may help to explain the decision bias in the classical newsvendor model. Then, to reduce the risk which originates from the fluctuation in the market demand, we achieve the optimal order quantity to maximize CVaR about utility for the loss-averse newsvendor who is risk-averse. We find that this optimal order quantity is smaller than the optimal order quantity to maximize the expected utility above and is decreasing in the confidence levelα. Further, it is proved that the expected utility under this optimal order quantity is decreasing in the confidence levelα, which verifies that low risk implies low return. Finally, a numerical example is given to illustrate the obtained results and some management insights are suggested for the loss-averse newsvendor model.

Adherence

2016 ◽  
Vol 5 (4) ◽  
pp. 41-53
Author(s):  
Michael Möcker ◽  
Klaus Mann
Keyword(s):  
Health Policy ◽  
Behavioral Economics ◽  
Loss Aversion ◽  
Ambiguity Aversion ◽  
Medical Advice ◽  
Libertarian Paternalism ◽  
Dynamic Inconsistency

Non-adherence to medical advice is a serious problem to patients, health policy and practitioners. This article outlines concepts of behavioral economics that might lead a patient to decide against the provider's recommendations and thus to be non-adherent. Especially the timing of pay-offs and dynamic inconsistency, their uncertainty and ambiguity aversion, loss-aversion and numerous heuristics like the peak-end-rule are discussed. The paper concludes with some hints on “libertarian” paternalism that may improve the situation.

Crowdsensing From the Perspective of Behavioral Economics: An Incentive Mechanism Based on Mental Accounting

2019 ◽  
Vol 6 (5) ◽  
pp. 9123-9139 ◽  
Author(s):  
Deng Li ◽  
Sihui Wang ◽  
Jiaqi Liu ◽  
Hui Liu ◽  
Sheng Wen
Keyword(s):  
Behavioral Economics ◽  
Incentive Mechanism ◽  
Mental Accounting

scholarly journals End-to-End Game-Focused Learning of Adversary Behavior in Security Games

2020 ◽  
Vol 34 (02) ◽  
pp. 1378-1386
Author(s):  
Andrew Perrault ◽  
Bryan Wilder ◽  
Eric Ewing ◽  
Aditya Mate ◽  
Bistra Dilkina ◽  
...  
Keyword(s):  
Expected Utility ◽  
Optimization Problem ◽  
Predictive Accuracy ◽  
Experimental Results ◽  
Limited Data ◽  
Two Stage ◽  
Security Games ◽  
End To End ◽  
Feature Values ◽  
Adversary Model

Stackelberg security games are a critical tool for maximizing the utility of limited defense resources to protect important targets from an intelligent adversary. Motivated by green security, where the defender may only observe an adversary's response to defense on a limited set of targets, we study the problem of learning a defense that generalizes well to a new set of targets with novel feature values and combinations. Traditionally, this problem has been addressed via a two-stage approach where an adversary model is trained to maximize predictive accuracy without considering the defender's optimization problem. We develop an end-to-end game-focused approach, where the adversary model is trained to maximize a surrogate for the defender's expected utility. We show both in theory and experimental results that our game-focused approach achieves higher defender expected utility than the two-stage alternative when there is limited data.

OPTIMAL PORTFOLIO UNDER STATE-DEPENDENT EXPECTED UTILITY

2018 ◽  
Vol 21 (03) ◽  
pp. 1850013 ◽  
Author(s):  
CAROLE BERNARD ◽  
STEVEN VANDUFFEL ◽  
JIANG YE
Keyword(s):  
Portfolio Choice ◽  
Expected Utility ◽  
Loss Aversion ◽  
Reference Point ◽  
Joint Distribution ◽  
Optimal Portfolio ◽  
Characterization Result ◽  
Terminal Wealth ◽  
Utility Maximizer ◽  
State Dependent

We derive the optimal portfolio for an expected utility maximizer whose utility does not only depend on terminal wealth but also on some random benchmark (state-dependent utility). We then apply this result to obtain the optimal portfolio of a loss-averse investor with a random reference point (extending a result of Berkelaar et al. (2004) Optimal portfolio choice under loss aversion, The Review of Economics and Statistics 86 (4), 973–987). Clearly, the optimal portfolio has some joint distribution with the benchmark and we show that it is the cheapest possible in having this distribution. This characterization result allows us to infer the state-dependent utility function that explains the demand for a given (joint) distribution.

A Bayesian method to estimate the optimal threshold of a marker used to select patients' treatment

2019 ◽  
Vol 29 (1) ◽  
pp. 29-43
Author(s):  
Yoann Blangero ◽  
Muriel Rabilloud ◽  
René Ecochard ◽  
Fabien Subtil
Keyword(s):  
Utility Function ◽  
Expected Utility ◽  
Treatment Options ◽  
Growth Factor Receptor ◽  
Credible Interval ◽  
Optimal Threshold ◽  
Selection Marker ◽  
Large Sample Size ◽  
Marker Distribution ◽  
Definition Of

The use of a quantitative treatment selection marker to choose between two treatment options requires the estimate of an optimal threshold above which one of these two treatments is preferred. Herein, the optimal threshold expression is based on the definition of a utility function which aims to quantify the expected utility of the population (e.g. life expectancy, quality of life) by taking into account both efficacy (success or failure) and toxicity of each treatment option. Therefore, the optimal threshold is the marker value that maximizes the expected utility of the population. A method modelling the marker distribution in patient subgroups defined by the received treatment and the outcome is proposed to calculate the parameters of the utility function so as to estimate the optimal threshold and its 95% credible interval using the Bayesian inference. The simulation study found that the method had low bias and coverage probability close to 95% in multiple settings, but also the need of large sample size to estimate the optimal threshold in some settings. The method is then applied to the PETACC-8 trial that compares the efficacy of chemotherapy with a combined chemotherapy + anti-epidermal growth factor receptor in stage III colorectal cancer.

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